A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model.     

Quadrilateral meshes with provable angle bounds

In this paper, we present an algorithm that utilizes a quadtree data structure to construct a quadrilateral mesh for a simple polygonal region in which no newly created angle is smaller than 18.43^° (=arctan(\frac13)){{18.43}}^{\circ} ({=}\hbox{arctan}(\frac{1}{3})) or greater than 171.86^° (=135^° + 2arctan(\frac13)){{171.86}}^{\circ} ({=}{{135}}^{\circ} + 2\hbox{arctan}(\frac{1}{3})). This is the first known result, to the best of our knowledge, on a direct quadrilateral mesh generation algorithm with a provable guarantee on the angles.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

Real imaging and size values of Saccharomyces cerevisiae cells with comparable contrast tuning to two environmental scanning electron microscopy modes

The combined application of electron microscopy (EM) is frequently used for the microstructural investigation of biological specimens and plays two important roles in the quantification and in gaining an improved understanding of biological phenomena by making use of the highest resolution capability provided by EM. The possibility of imaging wet specimens in their "native" states in the environmental scanning electron microscope (ESEM) at high resolution and large depth of focus in real time is discussed in this paper. It is demonstrated here that new features can be discovered by the elimination of even the least hazardous approaches in some preparation techniques, that destroy the samples. Since the analysis conditions may influence the morphology and the extreme surface sensitivity of living biological systems, the results obtained from the same cultured cell with two different ESEM modes (Lvac mode and wet mode) were compared. This offers new opportunities compared with ESEM-wet/Lvac-mode imaging, since wet-mode imaging involves a real contrast and gives an indication of the changes in cell morphology and structure required for cell viability. In this study, wet-mode imaging was optimized using the unique ability of cell quantities for microcharacterization in situ giving very fine features of topological effects. Accordingly, the progress is reported by comparing the results of these two modes, which demonstrate interesting application details. In general, the functional comparisons have revealed that the fresh unprocessed Saccharomyces cerevisiae cells (ESEM-wet mode) were essentially unaltered with improved and minimal specimen preparation timescales, and the optimal cell viability degree was visualized and also measured quantitatively while the cell size remained unchanged with continuous images.     

Next-Generation Grade and Survival Expression Biomarkers of Human Gliomas Based on Algorithmically Reconstructed Molecular Pathways

In gliomas, expression of certain marker genes is strongly associated with survival and tumor type and often exceeds histological assessments. Using a human interactome model, we algorithmically reconstructed 7494 new-type molecular pathways that are centered each on an individual protein. Each single-gene expression and gene-centric pathway activation was tested as a survival and tumor grade biomarker in gliomas and their diagnostic subgroups (IDH mutant or wild type, IDH mutant with 1p/19q co-deletion, MGMT promoter methylated or unmethylated), including the three major molecular subtypes of glioblastoma (proneural, mesenchymal, classical). We used three datasets from The Cancer Genome Atlas and the Chinese Glioma Genome Atlas, which in total include 527 glioblastoma and 1097 low grade glioma profiles. We identified 2724 such gene and 2418 pathway survival biomarkers out of total 17,717 genes and 7494 pathways analyzed. We then assessed tumor grade and molecular subtype biomarkers and with the threshold of AUC > 0.7 identified 1322/982 gene biomarkers and 472/537 pathway biomarkers. This suggests roughly two times greater efficacy of the reconstructed pathway approach compared to gene biomarkers. Thus, we conclude that activation levels of algorithmically reconstructed gene-centric pathways are a potent class of new-generation diagnostic and prognostic biomarkers for gliomas.     

Using spray-dried sugar beet molasses in ice cream as a novel bulking agent

In this study, it is aimed to investigate and evaluate the use of molasses which is a by-product of sugar production as a novel bulking agent in ice cream as a sugar replacer. Sugar beet molasses (75%) and 12 DE maltodextrin (25%) were converted into powder form by spray drying. Spray-dried sugar beet molasses (SDSM) was then used as a substitute of sugar in different ratios (0:100, 25:75, 50:50, 75:25 and 100:0) in the ice cream products. The increased amount of SDSM decreased the overrun, L*, whiteness index (WI) and melting behaviours and increased a*, b*, total phenolic content, consistency and viscosity. Meanwhile, thermal properties have not been affected by the use of SDSM (P < 0.05). The sensory findings have been particularly interesting especially when replacing above 50% sugar with SDSM, aroma, flavour and general acceptability decrease. According to the results of this study, substituting 25% of total sugar with SDSM as a bulking agent can decrease cost of the product and improve total phenolic content and some quality parameters without compromising sensorial properties.     

Surface‐modified carbon black derived from used car tires as alternative,reusable, and regenerable catalysts for H2 release studies from sodium borohydride methanolysis

Carbon black (CB) obtained from used car tire rubbers were treated with concentrated sulfuric and nitric acids. The oxidized CB (CB‐COO‐Na⁺) is subsequently modified with epichlorohydrin (ECH) and amines including polyethylene imine (PEI). These modified CBs such as CB‐PEI are used as metal‐free catalysts in methanolysis of sodium borohydride (NaBH₄) to produce hydrogen. The hydrogen generation rate (HGR) of 3089 ± 44.69 mL.min^‐1.g^‐1 is accomplished at room temperature with CB‐PEI‐hydrochloric acid (HCl) catalyst. The resulting activation energy of 34.7 kJ/mol for the temperature range of ?20°C to +30°C compares favorably to most of alternative catalysts reported in literature while reaction catalyzing capabilities of CB‐PEI‐HCl particles extend to the subzero temperature range (?20°C‐0°C). The reuse and regeneration studies conducted for the CB‐PEI‐HCl catalyst showed that these catalysts do provide complete conversion at every use up to five consecutive runs and retain 50 ± 2.5% of the original hydrogen generation rate at the fifth consecutive reuse. The CBs‐based catalysts are fully regenerated with HCl treatment.     

Response Surface Modeling and Evaluation of the Influence of Deposition Parameters on the Electrolytic Cu-Sn Alloy Powders Production

In this article, the electrodeposition process of Cu-Sn alloy powders from tripolyphosphate (TPP)-based electrolytes was investigated as a function of deposition parameters. The effects of deposition parameters such as current density, electrolyte composition (Cu/Sn mole ratio), mechanical stirring speed, and temperature on the Cu content of alloy powder and cathodic current efficiency were evaluated using the response surface methodology (RSM). The empirical models developed in terms of deposition parameters were found to be statistically adequate to describe the process responses. The study revealed that as far as the copper content was concerned in the alloyed powders, all parameters selected had positive correlations. However, a high stirring speed and low current density led to a greater current efficiency. The morphology and chemical composition of the electrodeposited Cu-Sn alloy powders were investigated using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and inductively coupled plasma (ICP) analysis. An SEM analysis showed that the powder morphology was affected considerably by the cathodic current density and stirring speed.